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Wavelet methods for time series analysis Andrew T. Walden, Donald B. Percival
Publisher: Cambridge University Press

- Wavelet Methods for Time Series Analysis, by Percival and Walden: standard theoretical text on wavelets. Filtering and wavelets and Fourier. Than the previous methods, the error is actually roughly the same as for all other options we tried out. Also, lossy method of image compression on the Mandelbrot set. When applied to time-series data, wavelet analysis involves a transform from the given one-dimensional time series to a two-dimensional time-frequency image. Analysis methods of investment are always the researching hotspot of financial field. Details of scaling and translation of the Morlet wavelet with an interactive Demonstration. Wavelet Methods for Time Series Analysis (Cambridge Series in Statistical and Probabilistic Mathematics) By Donald B. Frequency analysis and decompositions (Fourier-/Cosine-/Wavelet transformation) for example for forecasting or decomposition of time series; Machine learning and data mining, for example k-means clustering, decision trees, classification, feature selection; Multivariate analysis, correlation; Projections, prediction, future prospects But in order to derive ideas and guidance for future decisions, higher sophisticated methods are required than just sum/group by. Available time series prediction method is linear models such as AR and ARIMA, these models need people to determine the order and type, the subjective factor is relatively large and there is no way to nonlinear models for effective approximation. Through the difference or logarithm transform, the Not only avoid to inherent defects of neural network, but also together with the local approximation of wavelet analysis. Econometric Analysis, by Greene: classic text on theoretical econometrics.